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Full Bayesian wavelet inference with a nonparametric prior

Wang, Xue, Walker, Stephen G. (2013) Full Bayesian wavelet inference with a nonparametric prior. Journal of Statistical Planning and Inference, 143 (1). pp. 55-62. ISSN 0378-3758. (doi:10.1016/j.jspi.2012.05.010)

Abstract

In this paper,we introduce a new Bayesian nonparametric model for estimating an unknown function in the presence of Gaussian noise.The proposed model involves a mixture of a point mass and an arbitrary (nonparametric) symmetric and unimodal distribution for modeling wavelet coefficients.Posterior simulation uses slice sampling ideas and the consistency under the proposed model is discussed. In particular,the method is shown to be computationally competitive with some of best Empirical wavelet estimation methods.

Item Type: Article
DOI/Identification number: 10.1016/j.jspi.2012.05.010
Uncontrolled keywords: Stick-breaking priors; Slice sampling; Wavelet shrinkage; Consistency
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Xue Wang
Date Deposited: 05 Dec 2013 12:07 UTC
Last Modified: 14 Jun 2019 11:27 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/37209 (The current URI for this page, for reference purposes)
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